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Robert Half Data Scientist in Austin, Texas

Description Responsibilities: Data Collection: Gathering and preprocessing large datasets from various sources, including databases, APIs, and external data repositories. Data Cleaning and Preparation: Cleaning, transforming, and structuring raw data to ensure accuracy, completeness, and consistency for analysis. Exploratory Data Analysis (EDA): Conducting exploratory data analysis to understand patterns, trends, and relationships within the data. Statistical Analysis: Applying statistical techniques such as regression analysis, hypothesis testing, and clustering to analyze data and derive meaningful insights. Machine Learning: Developing and implementing machine learning models and algorithms to predict outcomes, classify data, or uncover patterns in the data. Feature Engineering: Creating new features or variables from existing data to improve the performance of machine learning models. Model Evaluation and Validation: Evaluating the performance of machine learning models using appropriate metrics and validating their accuracy and reliability. Data Visualization: Creating visualizations such as charts, graphs, and dashboards to communicate insights and findings effectively to stakeholders. Model Deployment: Deploying machine learning models into production environments and integrating them into existing systems or applications. Collaboration: Collaborating with cross-functional teams, including data engineers, software developers, and business stakeholders, to understand requirements and deliver data-driven solutions. Continuous Learning: Staying updated on the latest tools, techniques, and best practices in data science and machine learning to enhance skills and capabilities. Requirements Skills: Programming Languages: Proficiency in programming languages commonly used in data science, such as Python or R, for data manipulation, analysis, and modeling. Machine Learning Libraries: Experience with machine learning libraries and frameworks such as scikit-learn, TensorFlow, or PyTorch for building and deploying models. Statistical Analysis: Strong understanding of statistical concepts and techniques for analyzing data and deriving insights. Data Visualization Tools: Familiarity with data visualization tools such as Matplotlib, Seaborn, or Plotly for creating visualizations and communicating findings. Database Management: Knowledge of database management systems (DBMS) and SQL for querying and manipulating large datasets. Big Data Technologies: Understanding of big data technologies such as Hadoop, Spark, or Apache Kafka for working with large-scale datasets. Problem-Solving: Excellent analytical and problem-solving skills to tackle complex data-related challenges and develop innovative solutions. Communication: Strong communication and presentation skills to convey technical findings and insights to non-technical stakeholders. Teamwork: Ability to work effectively in cross-functional teams and collaborate with colleagues from diverse backgrounds. Qualifications: Education: A bachelor's degree in computer science, statistics, mathematics, or a related field is typically required. A master's degree or PhD in data science or a related discipline may be preferred for more advanced positions. Experience: Previous experience in data analysis, machine learning, or a related field is often required. Experience with specific industries or domains may also be beneficial. Certifications: While not always required, certifications in data science or machine learning (e.g., AWS Certified Machine Learning – Specialty, Google detail oriented Data Engineer) can be advantageous. Portfolio: A portfolio showcasing data science projects, including data analyses, machine learning models, and visualizations, can demonstrate skills and expertise to potential employers. Technology Doesn't Change the World, People Do.®

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